The Use of Electronic Data Capture Tools in Clinical Trials: Web-Survey of 259 Canadian Trials
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Electronic data capture (EDC) tools provide automated support for data collection, reporting, query resolution, randomization, and validation, among other features, for clinical trials. There is a trend toward greater adoption of EDC tools in clinical trials, but there is also uncertainty about how many trials are actually using this technology in practice. A systematic review of EDC adoption surveys conducted up to 2007 concluded that only 20% of trials are using EDC systems, but previous surveys had weaknesses. OBJECTIVES: Our primary objective was to estimate the proportion of phase II/III/IV Canadian clinical trials that used an EDC system in 2006 and 2007. The secondary objectives were to investigate the factors that can have an impact on adoption and to develop a scale to assess the extent of sophistication of EDC systems. METHODS: We conducted a Web survey to estimate the proportion of trials that were using an EDC system. The survey was sent to the Canadian site coordinators for 331 trials. We also developed and validated a scale using Guttman scaling to assess the extent of sophistication of EDC systems. Trials using EDC were compared by the level of sophistication of their systems. RESULTS: We had a 78.2% response rate (259/331) for the survey. It is estimated that 41% (95% CI 37.5%-44%) of clinical trials were using an EDC system. Trials funded by academic institutions, government, and foundations were less likely to use an EDC system compared to those sponsored by industry. Also, larger trials tended to be more likely to adopt EDC. The EDC sophistication scale had six levels and a coefficient of reproducibility of 0.901 (P< .001) and a coefficient of scalability of 0.79. There was no difference in sophistication based on the funding source, but pediatric trials were likely to use a more sophisticated EDC system. CONCLUSION: The adoption of EDC systems in clinical trials in Canada is higher than the literature indicated: a large proportion of clinical trials in Canada use some form of automated data capture system. To inform future adoption, research should gather stronger evidence on the costs and benefits of using different EDC systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.608 | 0.590 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it